Approximately 37% of children entering 2nd grade present with handwriting impairments (i.e., dysgraphia). Children with a wide range of developmental disabilities, particularly autism spectrum disorders (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and various learning disabilities, experience sustained difficulty with handwriting. In learning to write, children develop automaticity in handwriting, which minimizes the interference of motor demands with higher-order cognitive processes related to composition. Thus, the dynamics of writing account for a large portion of variance in composition fluency. Adults also present with handwriting difficulties and often handwriting is used to measure signs of motor impairments associated with neurodegenerative processes (e.g., resting tremor in Parkinson's disease). Due to the fundamental nature of handwriting, dysgraphia is one of the most common reasons for referral for occupational therapy.
For many years, handwriting assessment relied on manual methods with time consuming (often pain staking) analysis of letter form, size, and spacing that was subjective and only semi-quantitative. In recent years, computerized methods, involving recording from digitizing tablets, have been applied to more quantitative assessment of handwriting kinematics (e.g., speed, accelerations/decelerations); however, computerized assessment of letter form, which is one of, if not the most, crucial handwriting metric, have been lacking.
It would therefore be advantageous to provide a computer application to interventionists that could assess both kinematic and morphometric components of handwriting. This approach has been tested and shown to be sensitive to clinical differences in motor performance in ASD and ADHD. Therefore, this approach is not limited to readily implement and evaluate the efficacy of targeted interventions for handwriting. This approach could have a broader application. For example, it could be used in forensics to identify and individual's handwriting pattern, or to compare signatures to identify fraud or to provide easily accessible and implementable assessments of fine motor performance. This approach is able to assess any digital input and both analyze the kinematic and morphometric properties, thereby serving a broad set of applications.